U.S. patent application number 16/141223 was filed with the patent office on 2020-03-26 for live agent recommendation for a human-robot symbiosis conversation system.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Hao Chen, Christopher J. Davis, Ran Guan, Sharath Kancharla, Manon Knoertzer, Jie Ma, Rachel Mohammed, Zhongzheng Shu, Xin Zhou.
Application Number | 20200099790 16/141223 |
Document ID | / |
Family ID | 69883767 |
Filed Date | 2020-03-26 |
United States Patent
Application |
20200099790 |
Kind Code |
A1 |
Ma; Jie ; et al. |
March 26, 2020 |
LIVE AGENT RECOMMENDATION FOR A HUMAN-ROBOT SYMBIOSIS CONVERSATION
SYSTEM
Abstract
A computer-implemented method is presented for selecting a
preferred live agent from a plurality of live agents. The method
includes constructing, via the processor, a human expertise matrix
pertaining to each of the plurality of live agents by determining
an average net promoter score (NPS) for each of the plurality of
live agents for each category of a plurality of categories, and in
response to a voice call by a user, determining, via the processor,
a predicted human expertise on average by collectively assessing
the human expertise matrix, a predicted NPS derived from a first
deep neural network, and a predicted category derived from a second
deep neural network. The method further includes, based on the
predicted human expertise on average determined, triggering
communication via the live agent communication network between the
user and the preferred live agent to initiate a conversation
between the user and the preferred live agent.
Inventors: |
Ma; Jie; (Nanjing, CN)
; Zhou; Xin; (Beijing, CN) ; Chen; Hao;
(Beijing, CN) ; Mohammed; Rachel; (Wobrun, MA)
; Davis; Christopher J.; (Boulder, CO) ;
Kancharla; Sharath; (Malden, MA) ; Shu;
Zhongzheng; (Malden, MA) ; Knoertzer; Manon;
(Saint Martin le Vinoux, FR) ; Guan; Ran;
(Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Family ID: |
69883767 |
Appl. No.: |
16/141223 |
Filed: |
September 25, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/063112 20130101;
G10L 15/22 20130101; G06Q 30/016 20130101; G10L 15/16 20130101;
H04M 3/5233 20130101; G06N 3/0454 20130101; H04M 2203/555 20130101;
G10L 2015/223 20130101; H04M 2203/40 20130101 |
International
Class: |
H04M 3/523 20060101
H04M003/523; G06N 3/04 20060101 G06N003/04; G06Q 10/06 20060101
G06Q010/06; G06Q 30/00 20060101 G06Q030/00; G10L 15/16 20060101
G10L015/16; G10L 15/22 20060101 G10L015/22 |
Claims
1. A computer-implemented method executed on a processor for
selecting a preferred live agent from a plurality of live agents
linked within a live agent communication network, the
computer-implemented method comprising: constructing, via the
processor, a human expertise matrix pertaining to each of the
plurality of live agents by determining an average net promoter
score (NPS) for each of the plurality of live agents for each
category of a plurality of categories; in response to a voice call
by a user, determining, via the processor, a predicted human
expertise on average by collectively assessing the human expertise
matrix, a predicted NPS derived from a first deep neural network,
and a predicted category derived from a second deep neural network;
and based on the predicted human expertise on average determined,
triggering communication via the live agent communication network
between the user and the preferred live agent to initiate a
conversation between the user and the preferred live agent.
2. The method of claim 1, further comprising inputting a current
user query and current conversations before the current user query
to the first and second deep neural networks.
3. The method of claim 1, wherein the first deep neural network is
employed to compute the NPS and the second deep neural network is
employed to compute the category.
4. The method of claim 1, further comprising computing a loss
function for each of the first and second deep neural networks.
5. The method of claim 4, further comprising updating a neuron
weight of each of the first and second deep neural networks.
6. The method of claim 1, wherein the first and second deep neural
networks are employed to create a conversation categorization
model.
7. The method of claim 6, wherein a stochastic prediction of each
category for each of the plurality of agents is determined based on
the conversation categorization model.
8. A non-transitory computer-readable storage medium comprising a
computer-readable program executed on a processor in a data
processing system for selecting a preferred live agent from a
plurality of live agents, wherein the computer-readable program
when executed on the processor causes a computer to perform the
steps of: constructing, via the processor, a human expertise matrix
pertaining to each of the plurality of live agents by determining
an average net promoter score (NPS) for each of the plurality of
live agents for each category of a plurality of categories; in
response to a voice call by a user, determining, via the processor,
a predicted human expertise on average by collectively assessing
the human expertise matrix, a predicted NPS derived from a first
deep neural network, and a predicted category derived from a second
deep neural network; and based on the predicted human expertise on
average determined, triggering communication via the live agent
communication network between the user and the preferred live agent
to initiate a conversation between the user and the preferred live
agent.
9. The non-transitory computer-readable storage medium of claim 8,
wherein a current user query and current conversations before the
current user query are inputted into the first and second deep
neural networks.
10. The non-transitory computer-readable storage medium of claim 8,
wherein the first deep neural network is employed to compute the
NPS and the second deep neural network is employed to compute the
category.
11. The non-transitory computer-readable storage medium of claim 8,
wherein a loss function is computed for each of the first and
second deep neural networks.
12. The non-transitory computer-readable storage medium of claim
11, wherein a neuron weight is updated for each of the first and
second deep neural networks.
13. The non-transitory computer-readable storage medium of claim 8,
wherein the first and second deep neural networks are employed to
create a conversation categorization model.
14. The non-transitory computer-readable storage medium of claim
13, wherein a stochastic prediction of each category for each of
the plurality of agents is determined based on the conversation
categorization model.
15. A system for selecting a preferred live agent from a plurality
of live agents, the system comprising: a memory; and one or more
processors in communication with the memory configured to:
construct, via the processor, a human expertise matrix pertaining
to each of the plurality of live agents by determining an average
net promoter score (NPS) for each of the plurality of live agents
for each category of a plurality of categories; in response to a
voice call by a user, determine, via the processor, a predicted
human expertise on average by collectively assessing the human
expertise matrix, a predicted NPS derived from a first deep neural
network, and a predicted category derived from a second deep neural
network; and based on the predicted human expertise on average
determined, trigger communication via the live agent communication
network between the user and the preferred live agent to initiate a
conversation between the user and the preferred live agent.
16. The system of claim 15, wherein a current user query and
current conversations before the current user query are inputted
into the first and second deep neural networks.
17. The system of claim 15, wherein the first deep neural network
is employed to compute the NPS and the second deep neural network
is employed to compute the category.
18. The system of claim 15, wherein a loss function is computed for
each of the first and second deep neural networks.
19. The system of claim 18, wherein a neuron weight is updated for
each of the first and second deep neural networks.
20. The system of claim 15, wherein the first and second deep
neural networks are employed to create a conversation
categorization model.
Description
BACKGROUND
Technical Field
[0001] The present invention relates generally to connecting users
with agents, and more specifically, to a live agent recommendation
for a human-robot symbiosis conversation system.
Description of the Related Art
[0002] Companies provide traditional communications mechanisms such
as account managers, customer service representatives, subject
matter experts, interactive voice response (IVR) units, websites,
email, and live agents to handle user communications such as
comments, inquiries, complaints, recommendations, and
clarifications. Live agents, however, have limitations, such as
limited working hours, limited capacity, limited knowledge levels
or skill sets, etc., that make it impractical to guarantee, for
example, that the best live agent is always available to assist a
particular customer whenever that customer has a need to contact
the contact center, or that the same answer is provided to
different customers experiencing the same situation.
SUMMARY
[0003] In accordance with an embodiment, a method is provided for
selecting a preferred live agent from a plurality of live agents.
The method includes constructing, via the processor, a human
expertise matrix pertaining to each of the plurality of live agents
by determining an average net promoter score (NPS) for each of the
plurality of live agents for each category of a plurality of
categories, in response to a voice call by a user, determining, via
the processor, a predicted human expertise on average by
collectively assessing the human expertise matrix, a predicted NPS
derived from a first deep neural network, and a predicted category
derived from a second deep neural network, and based on the
predicted human expertise on average determined, triggering
communication via the live agent communication network between the
user and the preferred live agent to initiate a conversation
between the user and the preferred live agent.
[0004] In accordance with another embodiment, a system is provided
for selecting a preferred live agent from a plurality of live
agents. The system includes a memory and one or more processors in
communication with the memory configured to construct, via the
processor, a human expertise matrix pertaining to each of the
plurality of live agents by determining an average net promoter
score (NPS) for each of the plurality of live agents for each
category of a plurality of categories, in response to a voice call
by a user, determine, via the processor, a predicted human
expertise on average by collectively assessing the human expertise
matrix, a predicted NPS derived from a first deep neural network,
and a predicted category derived from a second deep neural network,
and based on the predicted human expertise on average determined,
trigger communication via the live agent communication network
between the user and the preferred live agent to initiate a
conversation between the user and the preferred live agent.
[0005] In accordance with yet another embodiment, a non-transitory
computer-readable storage medium comprising a computer-readable
program for selecting a preferred live agent from a plurality of
live agents is presented. The non-transitory computer-readable
storage medium performs the steps of constructing, via the
processor, a human expertise matrix pertaining to each of the
plurality of live agents by determining an average net promoter
score (NPS) for each of the plurality of live agents for each
category of a plurality of categories, in response to a voice call
by a user, determining, via the processor, a predicted human
expertise on average by collectively assessing the human expertise
matrix, a predicted NPS derived from a first deep neural network,
and a predicted category derived from a second deep neural network,
and based on the predicted human expertise on average determined,
triggering communication via the live agent communication network
between the user and the preferred live agent to initiate a
conversation between the user and the preferred live agent.
[0006] It should be noted that the exemplary embodiments are
described with reference to different subject-matters. In
particular, some embodiments are described with reference to method
type claims whereas other embodiments have been described with
reference to apparatus type claims. However, a person skilled in
the art will gather from the above and the following description
that, unless otherwise notified, in addition to any combination of
features belonging to one type of subject-matter, also any
combination between features relating to different subject-matters,
in particular, between features of the method type claims, and
features of the apparatus type claims, is considered as to be
described within this document.
[0007] These and other features and advantages will become apparent
from the following detailed description of illustrative embodiments
thereof, which is to be read in connection with the accompanying
drawings.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0008] The invention will provide details in the following
description of preferred embodiments with reference to the
following figures wherein:
[0009] FIG. 1 is an exemplary processing system for live agent
recommendations, in accordance with embodiments of the present
invention;
[0010] FIG. 2 is a block/flow diagram of an exemplary cloud
computing environment, in accordance with an embodiment of the
present invention;
[0011] FIG. 3 is a schematic diagram of exemplary abstraction model
layers, in accordance with an embodiment of the present
invention;
[0012] FIG. 4 is a block/flow diagram of an example system for
determining training time, in accordance with an embodiment of the
present invention;
[0013] FIG. 5 is a block/flow diagram of an example system for
determining running time, in accordance with an embodiment of the
present invention;
[0014] FIG. 6 is a block/flow diagram illustrating a method for
processing conversations, in accordance with an embodiment of the
present invention;
[0015] FIG. 7 is a block/flow diagram illustrating a method for
determining the training time, in accordance with an embodiment of
the present invention;
[0016] FIG. 8 is a block/flow diagram illustrating a method for
determining the running time, in accordance with an embodiment of
the present invention;
[0017] FIG. 9 is a block/flow diagram illustrating a method for
making a live agent recommendation, in accordance with an
embodiment of the present invention; and
[0018] FIG. 10 is a block/flow diagram illustrating the selection
of an agent with the highest overall net promoter score (NPS) for
answering a question, in accordance with an embodiment of the
present invention.
[0019] Throughout the drawings, same or similar reference numerals
represent the same or similar elements.
DETAILED DESCRIPTION
[0020] Embodiments in accordance with the present invention provide
methods and devices for implementing artificial intelligence in
live agent recommendations scenarios. In general, computer
solutions, and in particular algorithms and processes known as
artificial intelligence, are in use to an ever increasing extent by
companies wishing to communicate with clients or customers. The
main benefit is that the cost of implementing an artificial
intelligence solution is a fraction of the cost of employing people
to perform the same role. However, there are technical difficulties
in implementing such a system based on artificial intelligence. For
instance, while for simple queries the system can be relatively
efficient, in the case of more complex queries, or ones that have
never before been presented to the system, current solutions are
inadequate, as time and processing resources are wasted in attempts
to resolve the issues using existing artificial intelligence
techniques. This leads to a heavy burden on the system in terms of
the memory and processing resources needed, in addition to a poor
rate of user satisfaction.
[0021] The operation of existing contact centers lacks
sophisticated personalized service, especially in the self-service
mode through interactive voice response (IVR)-type interfaces.
Customers tend to prefer interacting with live agents versus the
limited and impersonalized service available from IVR interfaces.
However, live agents often lack the skills, or give inappropriate
or inconsistent assistance in contact center environments, where
customers often experience a different live agent with every
attempt to address a concern or problem.
[0022] Embodiments in accordance with the present invention provide
methods and devices for implementing machine learning or deep
neural network techniques to recommend the best suited or preferred
agent on hand to address these and other deficiencies of randomly
selecting live agents and/or IVR interfaces. The selection of an
appropriate or best suited or preferred agent for routing an
inbound call can be based on determining a highest overall net
promoter score (NPS) for a live agent from a database of live
agents. The live agent receiving the highest overall NPS can be
selected to field the call from the customer. In accordance with
the present invention, methods and devices provide for two deep
neural networks to be implemented, one for determining NPS and
another for determining categories or skills or tasks. This results
in two deep neural network outputs, that is, a predicted NPS value
and a predicted category distribution function.
[0023] It is to be understood that the present invention will be
described in terms of a given illustrative architecture; however,
other architectures, structures, substrate materials and process
features and steps/blocks can be varied within the scope of the
present invention. It should be noted that certain features cannot
be shown in all figures for the sake of clarity. This is not
intended to be interpreted as a limitation of any particular
embodiment, or illustration, or scope of the claims.
[0024] FIG. 1 is an exemplary processing system for live agent
recommendations, in accordance with embodiments of the present
invention.
[0025] The processing system includes at least one processor (CPU)
104 operatively coupled to other components via a system bus 102. A
cache 106, a Read Only Memory (ROM) 108, a Random Access Memory
(RAM) 110, an input/output (I/O) adapter 120, a network adapter
130, a user interface adapter 140, and a display adapter 150, are
operatively coupled to the system bus 102. Additionally, an
artificial intelligence module 160 can be connected to the system
bus 102. Moreover, a live agent recommendation system 162 can be
connected to the system bus 102 in order to execute a training time
module 164 and a running time module 166.
[0026] A storage device 122 is operatively coupled to system bus
102 by the I/O adapter 120. The storage device 122 can be any of a
disk storage device (e.g., a magnetic or optical disk storage
device), a solid state magnetic device, and so forth.
[0027] A transceiver 132 is operatively coupled to system bus 102
by network adapter 130.
[0028] User input devices 142 are operatively coupled to system bus
102 by user interface adapter 140. The user input devices 142 can
be any of a keyboard, a mouse, a keypad, an image capture device, a
motion sensing device, a microphone, a device incorporating the
functionality of at least two of the preceding devices, and so
forth. Of course, other types of input devices can also be used,
while maintaining the spirit of the present invention. The user
input devices 142 can be the same type of user input device or
different types of user input devices. The user input devices 142
are used to input and output information to and from the processing
system.
[0029] A display device 152 is operatively coupled to system bus
102 by display adapter 150.
[0030] Of course, the processing system for live agent
recommendations can also include other elements (not shown), as
readily contemplated by one of skill in the art, as well as omit
certain elements. For example, various other input devices and/or
output devices can be included in the system, depending upon the
particular implementation of the same, as readily understood by one
of ordinary skill in the art. For example, various types of
wireless and/or wired input and/or output devices can be used.
Moreover, additional processors, controllers, memories, and so
forth, in various configurations can also be utilized as readily
appreciated by one of ordinary skill in the art. These and other
variations of the processing system for live agent recommendations
are readily contemplated by one of ordinary skill in the art given
the teachings of the present invention provided herein.
[0031] FIG. 2 is a block/flow diagram of an exemplary cloud
computing environment, in accordance with an embodiment of the
present invention.
[0032] It is to be understood that although this invention includes
a detailed description on cloud computing, implementation of the
teachings recited herein are not limited to a cloud computing
environment. Rather, embodiments of the present invention are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
[0033] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model can include at least five
characteristics, at least three service models, and at least four
deployment models.
[0034] Characteristics are as follows:
[0035] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0036] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0037] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but can
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0038] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0039] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported, providing
transparency for both the provider and consumer of the utilized
service.
[0040] Service Models are as follows:
[0041] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0042] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0043] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0044] Deployment Models are as follows:
[0045] Private cloud: the cloud infrastructure is operated solely
for an organization. It can be managed by the organization or a
third party and can exist on-premises or off-premises.
[0046] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It can be managed by the organizations
or a third party and can exist on-premises or off-premises.
[0047] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0048] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0049] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
[0050] Referring now to FIG. 2, illustrative cloud computing
environment 250 is depicted for enabling use cases of the present
invention. As shown, cloud computing environment 250 includes one
or more cloud computing nodes 210 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone 254A, desktop
computer 254B, laptop computer 254C, and/or automobile computer
system 254N can communicate. Nodes 210 can communicate with one
another. They can be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 250 to offer
infrastructure, platforms and/or software as services for which a
cloud consumer does not need to maintain resources on a local
computing device. It is understood that the types of computing
devices 254A-N shown in FIG. 2 are intended to be illustrative only
and that computing nodes 210 and cloud computing environment 250
can communicate with any type of computerized device over any type
of network and/or network addressable connection (e.g., using a web
browser).
[0051] FIG. 3 is a schematic diagram of exemplary abstraction model
layers, in accordance with an embodiment of the present invention.
It should be understood in advance that the components, layers, and
functions shown in FIG. 3 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
[0052] Hardware and software layer 360 includes hardware and
software components. Examples of hardware components include:
mainframes 361; RISC (Reduced Instruction Set Computer)
architecture based servers 362; servers 363; blade servers 364;
storage devices 365; and networks and networking components 366. In
some embodiments, software components include network application
server software 367 and database software 368.
[0053] Virtualization layer 370 provides an abstraction layer from
which the following examples of virtual entities can be provided:
virtual servers 371; virtual storage 372; virtual networks 373,
including virtual private networks; virtual applications and
operating systems 374; and virtual clients 375.
[0054] In one example, management layer 380 can provide the
functions described below. Resource provisioning 381 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 382 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources can include application software licenses.
Security provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 383 provides access to the cloud computing environment for
consumers and system administrators. Service level management 384
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 385 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0055] Workloads layer 390 provides examples of functionality for
which the cloud computing environment can be utilized. Examples of
workloads and functions which can be provided from this layer
include: mapping and navigation 391; software development and
lifecycle management 392; virtual classroom education delivery 393;
data analytics processing 394; transaction processing 395; and a
live agent recommendation system 396.
[0056] FIG. 4 is a block/flow diagram of an example system for
determining training time, in accordance with an embodiment of the
present invention.
[0057] The system 400 for determining training time includes a
human expertise matrix 425 and a category prediction model 435.
[0058] The human expertise matrix 425 is learned under different
categories based on net promoter score (NPS). The human expertise
matrix 425 includes an NPS for each agent based on a plurality of
categories or skills or topics. The NPS can be determined based on
one or more performance indicators.
[0059] Net Promoter or Net Promoter Score (NPS) is a management
tool that can be employed to gauge the loyalty of a firm's customer
relationships. NPS serves as an alternative to traditional customer
satisfaction research and claims to be correlated with revenue
growth. NPS is a metric that is derived from a question, such as,
how likely is it that a respondent would recommend a company,
product, and/or service to a friend or colleague? Respondents can
be categorized as promoters, passives, or detractors. Promoters can
include those respondents who respond with a score of 9 or 10
(e.g., on a scale of 1-10) and are considered loyal enthusiasts.
Detractors can include those respondents who respond with a score
of 0 to 6 (e.g., dissatisfied respondents). Passives can include
those respondents who respond with a score of 7 or 8 (e.g.,
respondents who do not directly affect the NPS). NPS can be
determined by subtracting the percentage of respondents who are
detractors from the percentage of respondents who are
promoters.
[0060] The one or more performance indicators of the live agents
can include, but are not limited to, data which indicates the
performance of a given agent such as average handle time (refers to
the time the agent spends on a call resolving one or more issues),
customer satisfaction survey ratings (e.g., which might be
developed at the conclusion of a call or via on-line), attendance
(refers to the presence of the agent in a capacity to work either
physically, virtually, or remotely), work performance ratings
(which can be developed based on subjective criteria, objective
criteria, by a supervisor or otherwise), script compliance (this
measures how often and how far the agent deviates from the prepared
script and whether such deviations are desirable or not), workflow
efficiency (this refers to processing time and the agent's ability
to utilize tools correctly and efficiently to resolve customer
issues), emotion events detected during a customer interaction
(this refers to either a manual or automated notation, regarding an
emotion which can utilize variations in volume, spoken words, etc.,
in conjunction with natural language understanding, review of
recorded interactions, etc.), and a quality rating for the customer
interaction (this refers to a calculation regarding the overall
quality of the interaction as opposed to the agent's specific
quality--it can take into account the difficulty level of the
interaction as well as the customer satisfaction with the
resolution).
[0061] Further, the category prediction model 435 can be learned by
employing deep neural network structure 410. In particular, user
queries, and subsequent messages described hereafter between users
and live agents/virtual assistants, could be in the form of typed
text and/or voice, and are, for example, transmitted to a routing
module and on to their destination in the form of data packets.
Each data packet, for example, includes a header indicating an
identifier of the user device from which the query or voice call
originates. The user queries or voice calls can be processed by the
deep neural network structure 410.
[0062] The term "category" can refer to or pertain to the skills of
an agent or to tasks completed by an agent. The skills exhibit the
experience or competency or proficiency or aptitude or savviness or
know-how of the agent in a variety of topics. The term "category"
can refer to topics or subjects or fields or issues that the agent
is proficient in. For example, an agent can be proficient in a
variety of topics or categories related to a product. The product
can be, e.g., a smart phone. The agent can be proficient in a
variety of topics or categories for smart phones, such as, battery
drain issues, frozen or slow interface, connectivity issues,
stalled text messages, overheating, synching errors, app crashing,
unresponsive screen, download issues, bad autocorrect suggestions,
etc. One agent can have fielded several queries regarding the
connectivity issues and stalled text messages topics or categories.
This agent would probably have a high average NPS related to such
topics, assuming he/she received ample positive feedback. As such,
if a new query or voice call is received dealing with one of those
issues or topics or categories, such agent with the highest average
NPS in such categories would be selected as the preferred agent to
field the new query or voice call.
[0063] Moreover, the task categories are evaluated based on
multi-round conversations between the user and the system. Before
the agent recommendation, the task could have multiple categories
and each category can require a different skill set. In most cases,
the category cannot be determined. However, the probability
distribution of the category can be estimated and an agent with the
best overall performance can be recommended by prediction over that
distribution. It is noted that the exemplary embodiments pertain to
a textual-based conversation system.
[0064] The deep neural network structure 410 can be part of an
artificial intelligence module.
[0065] The artificial intelligence module for example includes an
artificial intelligence engine having the functions of: natural
language interaction, allowing spoken or written natural language
received from a user to be interpreted, and natural language
responses to be generated; a dynamic decision module corresponding
to the function of making decisions, based on rules, on how to
respond to user queries; an interaction memory, storing a history
of interactions between a user and a live agent/virtual assistant,
for example including messages sent to and from the user; and a
behavior analysis function, which can include an algorithm for
detecting certain aspects of the interaction with the user, such as
emotion, which could indicate when a customer is not satisfied.
[0066] Thus, a machine learning component can be employed that
uses, e.g., post-screening, post-training information about an
agent and a pool of agents to optimize the deep neural networks
based on new, empirical information about agent performance.
[0067] As a broad subfield of artificial intelligence, machine
learning is concerned with the design and development of algorithms
and techniques that allow computers to "learn." At a general level,
there are two types of learning: inductive, and deductive.
Inductive machine learning methods extract rules and patterns out
of massive data sets. The major focus of machine learning research
is to extract information from data automatically by computational
and statistical methods, hence, machine learning is closely related
to data mining and statistics. Embodiments of machine learning can
appear in "supervised adaption" and "adaption of algorithms" to
evaluate agent performance and assign a score to each agent based
on prior performance.
[0068] An agent can be selected based on a skill set or expertise
of the agent, as well as other factors such as geographic location,
of the agent. The term "agent," "specialist," or "expert" refers to
a service center personnel or a computerized application, in some
cases, that respond to customer requests. An agent can be locally
situated at the service center or remotely situated over a network.
Throughout this application, the terms of "agent," "specialist,"
and "expert" are interchangeable terms dependent upon the
circumstances. In most cases, the term of "agent" collectively
refers to a customer representative, a support agent, a support
specialist, a support expert, or a combination thereof, which can
be a service center personnel and/or a computerized
application.
[0069] A service center can be implemented in a centralized
facility or server. Alternatively, a service center can be
implemented in multiple facilities or servers in a distributed
manner (e.g., cloud-based service platforms). A service center can
provide services to a variety of products or services from a
variety of clients or vendors. A client can be a manufacturer, a
distributor, a retailer, a service provider or broker, a purchasing
facility or a combination thereof. In one embodiment, a service
center can include service APIs to communicate with other systems
such as mobile devices, client sites, social communities, contact
centers including agents or experts, client backend systems,
manufacturer backend systems, eCommerce sites and other auxiliary
systems (e.g., billing system). A service center can handle service
requests from customers of multiple clients.
[0070] Note that a service center described throughout this
application is not limited to a traditional service center or
support center, nor is it implemented in a single physical
location. A service center described herein represents a collection
of service logic or providers communicatively coupled to each other
over a network in a distributed or a cloud-based fashion. The term
of a service center herein represents any kind of service providers
that provide a variety of services to customers or users.
[0071] In one embodiment, a data warehouse can include a product
database, a client database, a user database, and a knowledgebase.
Product database can be configured to store any data related to the
registered products including user manuals, etc. Client database
can be configured to store information related to clients such as
client's preferred communications mechanisms. User database can be
employed to store information related users, such as, for example,
registered products associated with a user, communications channel
preference of a user, credentials necessary for a user to access
other sites, and/or messaging filtering settings of a user, etc.
Knowledgebase can be employed to store knowledge collected and
compiled over a period of time, which can be used by agents and/or
users for self-support purposes.
[0072] In one embodiment, the service center further includes a
multi-channel communication and routing system to provide one or
more communication channels to any user or client to concurrently
access the service center. Examples of communication channels
include email, chat, texting (e.g., short messaging services or
SMS), voice (e.g., automated IVR, real-time, or VoIP), video, Web
(e.g., Web conferencing), and/or online community forum (e.g.,
Facebook.TM. or Twitter.TM.), etc. Note that the multi-channel
communication and routing system can be fully or partially
integrated with the service center or alternatively, it can be
maintained or provided by a third party or partner (e.g.,
communicatively coupled via service API over a network).
[0073] FIG. 5 is a block/flow diagram of an example system for
determining running time, in accordance with an embodiment of the
present invention.
[0074] The system 500 for determining running time includes
employing the human expertise matrix 425 with a predicted category
distribution 525 to calculate predicted human expertise 535 on
average to allow the human transfer engine 540 to select the
preferred live agent to answer the query or voice call from a
customer/user.
[0075] FIG. 6 is a block/flow diagram illustrating a method for
processing conversations, in accordance with an embodiment of the
present invention.
[0076] The conversation processing flow 600 can be described as
follows:
[0077] A user query 610 is received by, e.g., a call center. The
user query 610 can be broken down into sentences 612. The sentences
612 can be broken down into word vectors 614 by word embedding
techniques. In other words, words or phrases from the sentences 612
are mapped to vectors 614 of real numbers. The word vectors 614 are
then fed into a first deep neural network 616 and are also fed into
a second deep neural network 626. The first neural network 616 can
process NPS data, whereas the second neural network 626 can process
category or skill data. The first neural network 616 outputs an NPS
value 618, whereas the second neural network 626 outputs category
data 628. Moreover, the conversation 620 before the query 610 is
also analyzed. The conversation 620 before the query 610 can be
broken down into an array of sentences 622. The array of sentences
622 can be broken down into an array of word vectors 624 by word
embedding techniques. The array of word vectors 624 are then fed
into the first deep neural network 616 and are also fed into the
second deep neural network 626. The first neural network 616 can
process NPS data, whereas the second neural network 626 can process
category or skill data in view of the word vectors 614 from the
user query 610 and the array of word vectors 624 from the
conversation 620 before the query 610. The first neural network 616
outputs an NPS value 618, whereas the second neural network 626
outputs category data 628 based on both the word vectors 614 from
the query 610 and the array of word vectors 624 from the
conversation 620 before the query 610. Therefore, there are two
deep neural networks 616, 626 and two kinds of deep neural network
outputs 618, 628. Each neural network 616, 626 has two inputs, that
is, a current user query 610 and the current running conversation
620 before the user query 610.
[0078] FIG. 7 is a block/flow diagram 700 illustrating a method for
determining the training time, in accordance with an embodiment of
the present invention.
[0079] At block 710, the historical data related to each agent is
collected. The historical data can be, e.g., feedback received from
previous customers.
[0080] At block 712, the ID, the NPS, and the categories related to
an agent are obtained.
[0081] At block 714, the average NPS can be computed for an
agent.
[0082] At block 716, it is determined whether the data has been
collected for all the agents. If YES, the process proceeds to block
718 where the human expertise matrix is determined to construct the
first deep neural network, that is, the NPS neural network. If NO,
the process proceeds to block 710 to obtain the data/information
for the remaining agents.
[0083] At block 720, after the human expertise matrix has been
constructed, the deep learning training commences to construct the
first neural network. At block 720, the historical data related to
an agent is collected.
[0084] At block 722, obtain each user query that includes NPS
feedback in the historical data of block 720.
[0085] At block 600, the dataflow 600 of FIG. 6 is executed.
[0086] At block 724, a loss function is computed. The loss function
is computed by comparing the output of the neural networks and the
historical data.
[0087] At block 726, the neuron weight is updated. The neuron
weights are updated to optimize the accuracy of the neural
networks.
[0088] At block 728, it is determined whether the data has been
collected for all the agents. If YES, the process proceeds to block
730 where the second deep neural network is constructed, that is,
the category deep neural network. If NO, the process proceeds to
block 720 to obtain the data/information for the remaining
agents.
[0089] Therefore, in blocks 720-728, the NPS deep neural network is
constructed based on all the live agents. Thus, block 720-728
process NPS data.
[0090] At block 730, the deep learning training continues to
construct the second neural network. At block 730, the historical
data related to an agent is collected.
[0091] At block 732, obtain the conversations before the query in
block 722.
[0092] At block 600, the dataflow 600 of FIG. 6 is executed.
[0093] At block 734, a loss function is computed. The loss function
is computed by comparing the output of the neural networks and the
historical data.
[0094] At block 736, the neuron weight is updated. The neuron
weights are updated to optimize the accuracy of the neural
network.
[0095] At block 738, it is determined whether the data has been
collected for all the agents. If YES, the process ends because both
the first and second deep neural networks have been constructed
based on all live agents. If NO, the process proceeds to block 730
to obtain the data/information for the remaining agents.
[0096] Therefore, in blocks 730-738, the category or skills deep
neural network is constructed based on all the live agents. Thus,
block 730-738 process category or skill data.
[0097] Therefore, the flowchart 700 includes 3 parts. The first
part (blocks 710-718) pertains to the human expertise analytics,
the second part (blocks 720-728) pertains to the NPS deep learning
training, and the third part (blocks 730-738) pertains to the
category deep learning training.
[0098] FIG. 8 is a block/flow diagram 800 illustrating a method for
determining the running time, in accordance with an embodiment of
the present invention.
[0099] At block 810, the current query and the running conversation
are obtained.
[0100] At block 600, the dataflow 600 of FIG. 6 is executed.
[0101] At block 812, the predicted NPS is computed. The predicted
NPS is computed from the NPS deep neural network (blocks 720-728 of
FIG. 7).
[0102] At block 814, the predicted category is computed. The
predicted category is computed from the category deep neural
network (blocks 730-738 of FIG. 7).
[0103] At block 816, the human expertise matrix (blocks 710-718 of
FIG. 7) is also employed in cooperation with the predicted NPS at
block 812 and the predicted category at block 814 to determine the
predicted human expertise on average.
[0104] At block 820, the predicted human expertise on average is
determined.
[0105] At block 818, it is determined whether the output is the
lowest predicted NPS. If NO, then the process proceeds back to
block 810. If YES, the process proceeds to block 822.
[0106] At block 822, the agent is selected as the preferred agent
with the highest predicted or calculated NPS. In other words, this
is the agent selected to answer the query posed by the consumer or
user.
[0107] FIG. 9 is a block/flow diagram illustrating a method for
making a live agent recommendation, in accordance with an
embodiment of the present invention.
[0108] At block 910, the historical live agent expertise matrix is
learned under different categories based on NPS. The live agent
expertise matrix is computed for each agent. The live agent
expertise matrix includes the computed average NPS for each
category of a plurality of categories.
[0109] At block 912, the conversation categorization model is
learned by employing deep learning. The deep learning can include
employing two deep learning networks, one for NPS and another for
categories.
[0110] At block 914, the stochastic prediction of task category is
calculated based on the categorization model. The task categories
are evaluated based on multi-round conversations between users and
the system.
[0111] At block 916, the predicted overall NPS for each live agent
is calculated. The agent with the highest overall NPS is selected
to answer the query posed by a user/consumer.
[0112] FIG. 10 is a block/flow diagram illustrating the selection
of an agent with the highest overall NPS for answering a question,
in accordance with an embodiment of the present invention.
[0113] The system 1000 can include a user 1010 that establishes a
communication line 1015 with a IVR-type interface 1020. In one
instance, the user 1010 is not able to obtain a satisfactory answer
from the IVR-type interface 1020. As a result, a live agent needs
to be selected to answer the user or customer's 1010 question. A
plurality of live agents 1030 can be available to answer the
customer's 1010 question. However, a determination needs to be made
as to which live agent 1030 to select. The live agent
recommendation system 162 can aid in the selection of the live
agent 1030. The live agent recommendation system 162 can employ the
training time module 164 and the running time module 166. The
training time module 164 employs the structure 400 (FIG. 4) and the
running time module 166 employs the structure 500 (FIG. 5). The
live recommendation system 162 computes an overall NPS for each
agent 1030. The first live agent can have an overall NPS of 74, the
second live agent can have an overall NPS of 85, and the third live
agent can have an overall NPS of 91. As such, the system 162
selects the third agent having the overall NPS of 91, which is the
highest score of all the agents available at this time. Thus, the
third agent is deemed to be best suited or most-highly equipped or
preferred agent to answer the question by the customer 1010. The
preferred agents can be those having previous experiences with the
user and having ratings rated by the user, for example, higher than
a predetermined threshold.
[0114] In system 1000, inbound and outbound calls from and to the
end users devices of end users 1010 can traverse a telephone,
cellular, and/or data communication network depending on the type
of device that is being used. For example, the communications
network can include a private or public switched telephone network
(PSTN), local area network (LAN), private wide area network (WAN),
and/or public wide area network such as, for example, the Internet.
The communications network can also include a wireless carrier
network including a code division multiple access (CDMA) network,
global system for mobile communications (GSM) network, and/or any
3G or 4G network conventional in the art, and/or an LTE or any
future public communication network.
[0115] A user 1010 (also referred to herein as a customer) can
activate an application from the user's mobile device to reach
agents of the service center via a variety of communication
channels or media, such as, for example, email, chat, voice
(including automated interactive voice recognition or IVR, voice
over Internet protocol or VoIP), video, Web, and/or online
community-based forum, etc. The application can be a thin/thick
client application or a Web-based application.
[0116] According to one exemplary embodiment of the present
invention, a contact center can include an intelligent automated
agent 1020 for handling calls or other interactions (e.g., web)
with customers. The automated agent 1020 can be implemented, for
example, on a server. The automated agent 1020, for example, can
include capabilities, such as voice recognition, speech
recognition, answer generation, speech generation, and customer
profile information that enables the automated agent 1020 to
perform agent roles without having to use a live agent. For
instance, in one embodiment, the automated agent 1020 can maintain
a database of customer profile information (for example, as stored
on a nonvolatile storage device, such as a disk drive or mass
storage device) that can be updated with each interaction between
the customer and the contact center.
[0117] Furthermore, according to one embodiment, at a conclusion of
the live support session, a survey module can be configured to
transmit a survey to remote device to allow the user 1010 to
provide a feedback, such as a rating, concerning quality of the
selected agent 1030. The feedback can be employed to update the
ratings of the selected agent in general, as well as a rating of
the agent as a preferred agent associated with the user. Such a
rating can affect a subsequent selection of recommended agent
candidates and preferred agent candidates. Therefore, after each of
the support sessions, a user 1010 will be given the opportunity to
provide feedback on the specialist which can be seen by the next
user requesting a similar support need or themselves as they engage
in future support sessions. Rating feedback is cumulative for a
specialist and establishes a specialist as a skilled professional
as rated by the people he/she supports. This form of "crowd" based
feedback ensures that the specialist maintains a level of
professionalism, sensitivity to the nature of the users problem and
instilling confidence in the resolutions provided to the user.
[0118] In summary, the exemplary embodiments of the present
invention recommend human agents based on stochastic prediction of
task category in a human-robot symbiosis conversation system. The
exemplary embodiments provide for a result-driven approach that
maximizes NPS based on human agent performance under different
categories. Thus, live agent recommendation can be achieved for
conversations with multiple task categories. In other words, for a
conversational system, in the case where robots cannot handle the
questions adequately, the conversation should transfer to a human
agent for further service. However, human agents are not equally
capable of handling all types of questions. Live agent
recommendation is the key to the success of the human agent
transfer. The issue is to whom the conversation should be
transferred to, in order to maximize the overall service
quality.
[0119] To maximize the overall service, live agent recommendation
is result driven to maximize the net promoter score (NPS), in the
following steps: learn live agent expertise matrix under different
categories based on NPS, learn conversation categorization model
using deep learning, calculate the stochastic prediction of task
category based on the categorization model, and calculate the
predicted overall NPS for each live agent and make a
recommendation.
[0120] Therefore, the exemplary embodiments make recommendations
based on a stochastic prediction of a task category in a
human-robot symbiosis conversation system. In a human symbiosis
conversational system, the running task category is not static.
Instead, the running task often varies in a dynamic and stochastic
way, especially when multiple categories overlap with each other.
The recommendation system of the present exemplary embodiments
solves the issue where dynamic tasks are distributed in a
probability space.
[0121] In a complex situation, a conversation session can include
multiple tasks, which need a certain expertise matrix for a live
agent. The best or preferred live agent is the one who is good at
overall performance for multiple types of tasks. The exemplary
embodiments provide support for multiple task scoring based on
historical performance for different categories. This result-driven
approach makes recommendations to maximize NPS based on human agent
performance under difference categories, which is a quantitative
end-to-end method. Based on deep learning and historical NPS data,
this approach not only provides the best or preferred agent, but
also the predicted scores for all the live agents. This
recommendation approach provides users with an in-depth flexibility
of choosing agents upon the availability and geometric locations,
and explainability of comparing and choosing a live agent.
[0122] In one or more embodiments, a graphical user interface (GUI)
presentation of information about the specialists can be presented
to users that they can utilize to select the specialist of their
choice from a list of specialists that receive a high NPS. Examples
of such information includes (but not limited to): a) specialist
photo; b) specialist brief biography; c) current level rating based
on user feedback (rating); d) current availability (e.g.,
available, currently handling another user); and e) expected wait
time for a specialist (based on analysis of all other users who can
have queued up to for support from a specialist as well as his/her
average handle time of an interaction).
[0123] To provide for interaction with a user, embodiments of the
subject matter described in this specification can be implemented
on a computer having a display device, e.g., a CRT (cathode ray
tube) or LCD (liquid crystal display) monitor, for displaying
information to the user and a keyboard and a pointing device, e.g.,
a mouse or a trackball, by which the user can provide input to the
computer. Other kinds of devices can be used to provide for
interaction with a user as well; for example, feedback provided to
the user can be any form of sensory feedback, e.g., visual
feedback, auditory feedback, or tactile feedback; and input from
the user can be received in any form, including acoustic, speech,
or tactile input. In addition, a computer can interact with a user
by sending documents to and receiving documents from a device that
is used by the user; for example, by sending web pages to a web
browser on a user's client device.
[0124] The present invention can be a system, a method, and/or a
computer program product. The computer program product can include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0125] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
can be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0126] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network can include copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0127] Computer readable program instructions for carrying out
operations of the present invention can be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions can execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer can be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection can be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) can execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0128] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0129] These computer readable program instructions can be provided
to at least one processor of a general purpose computer, special
purpose computer, or other programmable data processing apparatus
to produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks or
modules. These computer readable program instructions can also be
stored in a computer readable storage medium that can direct a
computer, a programmable data processing apparatus, and/or other
devices to function in a particular manner, such that the computer
readable storage medium having instructions stored therein includes
an article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks or modules.
[0130] The computer readable program instructions can also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational
blocks/steps to be performed on the computer, other programmable
apparatus or other device to produce a computer implemented
process, such that the instructions which execute on the computer,
other programmable apparatus, or other device implement the
functions/acts specified in the flowchart and/or block diagram
block or blocks or modules.
[0131] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams can represent
a module, segment, or portion of instructions, which includes one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks can occur out of the order noted in
the figures. For example, two blocks shown in succession can, in
fact, be executed substantially concurrently, or the blocks can
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0132] Reference in the specification to "one embodiment" or "an
embodiment" of the present principles, as well as other variations
thereof, means that a particular feature, structure,
characteristic, and so forth described in connection with the
embodiment is included in at least one embodiment of the present
principles. Thus, the appearances of the phrase "in one embodiment"
or "in an embodiment", as well any other variations, appearing in
various places throughout the specification are not necessarily all
referring to the same embodiment.
[0133] It is to be appreciated that the use of any of the following
"/", "and/or", and "at least one of", for example, in the cases of
"A/B", "A and/or B" and "at least one of A and B", is intended to
encompass the selection of the first listed option (A) only, or the
selection of the second listed option (B) only, or the selection of
both options (A and B). As a further example, in the cases of "A,
B, and/or C" and "at least one of A, B, and C", such phrasing is
intended to encompass the selection of the first listed option (A)
only, or the selection of the second listed option (B) only, or the
selection of the third listed option (C) only, or the selection of
the first and the second listed options (A and B) only, or the
selection of the first and third listed options (A and C) only, or
the selection of the second and third listed options (B and C)
only, or the selection of all three options (A and B and C). This
can be extended, as readily apparent by one of ordinary skill in
this and related arts, for as many items listed.
[0134] Having described preferred embodiments of a system and
method for a live agent recommendation for a human-robot symbiosis
conversation system (which are intended to be illustrative and not
limiting), it is noted that modifications and variations can be
made by persons skilled in the art in light of the above teachings.
It is therefore to be understood that changes may be made in the
particular embodiments described which are within the scope of the
invention as outlined by the appended claims. Having thus described
aspects of the invention, with the details and particularity
required by the patent laws, what is claimed and desired protected
by Letters Patent is set forth in the appended claims.
* * * * *